Visibility distance estimation using deep learning in autonomous machine applications

ABSTRACT

In various examples, systems and methods are disclosed that use one or more machine learning models (MLMs) - such as deep neural networks (DNNs) - to compute outputs indicative of an estimated visibility distance corresponding to sensor data generated using one or more sensors of an autonomous or semi-autonomous machine. Once the visibility distance is computed using the one or more MLMs, a determination of the usability of the sensor data for one or more downstream tasks of the machine may be evaluated. As such, where an estimated visibility distance is low, the corresponding sensor data may be relied upon for less tasks than when the visibility distance is high.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Non-Provisional Application No.16/570,187, filed on Sep. 13, 2019, which is hereby incorporated byreference in its entirety.

BACKGROUND

Autonomous driving systems and semi-autonomous driving systems (e.g.,advanced driver assistance systems (ADAS)) may leverage sensors (e.g.,cameras, LiDAR sensors, RADAR sensors, etc.) to perform various tasks -such as blind spot monitoring, automatic emergency braking, lanekeeping, object detection, obstacle avoidance, and localization. Forexample, for autonomous and ADAS systems to operate independently andefficiently, an understanding of the surrounding environment of thevehicle in real-time or near real-time may be generated. To accuratelyand efficiently understand the surrounding environment of the vehicle,the sensors must generate usable, unobscured sensor data (e.g.,representative of images, depth maps, point clouds, etc.). However, asensor’s ability to perceive the surrounding environment may becompromised by a variety of sources - such as weather (e.g., rain, fog,snow, hail, smoke, etc.), traffic conditions, sensor blockage (e.g.,from debris, moisture, etc.), or blur. As a result, the resulting sensordata may not clearly depict vehicles, obstacles, and/or other objects inthe environment.

Conventional systems for addressing compromised visibility distanceshave used feature-level approaches to detect individual pieces of visualevidence, and subsequently pieced these features together to determinethat a compromised visibility exists. These conventional methodsprimarily rely on computer vision techniques - such as by analyzing theabsence of sharp edge features (e.g., sharp changes in gradient, color,intensity) in regions of the image, using color-based pixel analysis orother low-level feature analysis to detect potential visibility issues,and/or binary support vector machine classification with a blind versusnot blind output. However, such feature-based computer vision techniquesrequire separate analysis of each feature - e.g., whether each featureis relevant to visibility or not - as well as an analysis of how tocombine the different features for a specific sensor reduced visibilitycondition, thereby limiting the scalability of such approaches due tothe complexity inherent to the large variety and diversity of conditionsand occurrences that can compromise data observed using sensors inreal-world situations. For example, due to the computational expense ofexecuting these conventional approaches, they are rendered ineffectivefor real-time or near real-time deployment.

Further, conventional systems may rely on classifying reduced sensorvisibility causes - such as rain, snow, fog, glare, etc. - but may notprovide an accurate indication of the usability of the sensor data. Forexample, identifying rain in an image may not be actionable by thesystem for determining whether the corresponding image - or a portionthereof - is usable for various autonomous or semi-autonomous tasks. Insuch an example, where rain is present, the image may be deemed unusableby conventional systems, even though the image may clearly depict theenvironment within 100 meters of the vehicle. As such, instead ofrelying on the image for one or more tasks within the visible range, theimage may be mistakenly discarded and the one or more tasks may bedisabled. In this way, by treating each type of compromised sensorvisibility equally, less egregious or detrimental types of sensor maycause an instance of sensor data to be deemed unusable even where thisdetermination may not be entirely accurate (e.g., an image of anenvironment where a light drizzle is present may be usable for one ormore operations while an image of an environment with dense fog maynot).

SUMMARY

Embodiments of the present disclosure relate to deep neural networkprocessing for visibility distance estimation - e.g., a furthestdistance from a sensor that objects or elements may be discerned - inautonomous machine applications. Systems and methods are disclosed thatuse one or more machine learning models - such as deep neural networks(DNNs) - to compute outputs indicative of an estimated visibilitydistance (e.g., in the form of a computed distance or a distance binincluding a range of distances) corresponding to one or more sensors ofan autonomous or semi-autonomous machine. For example, by predicting anestimated visibility distance, the reliance of the machine on associatedsensor data for one or more downstream tasks - such as object detection,object tracking, obstacle avoidance, path planning, control decision,and/or the like - may be adjusted. As such, where an estimatedvisibility distance is low - e.g., 20 meters or less - the correspondingsensor data may only be relied upon for Level 0 (no automation) or Level1 (driver assistance) tasks (e.g., according to the Society ofAutomotive Engineers (SAE) automation levels), or may be relied upononly for predictions that are within 20 meters of the machine (e.g., andpredictions beyond 20 meters may be disregarded, or more have a lowerassociated confidence). Similarly, as another example, where anestimated visibility distance is high - e.g., 1000 meters or more - thecorresponding sensor data may be relied upon for the full performance ofLevel 3 (conditional driving automation) and Level 4 (high drivingautomation) tasks, or may be relied upon for predictions correspondingto locations within 1000 meters of the machine.

In this way, and in contrast to conventional systems, such as thosedescribed above, the systems and methods of the present disclosure maybe used to not only determine a usability of sensor data, but todetermine a level or degree of usability of the sensor data as definedusing a visibility distance - or a visibility distance bin havingassociated ranges of visibility distances. To train a machine learningmodel - e.g., a DNN - to accurately compute outputs representative ofthe visibility distance, the DNN may be trained using real-world data,augmented real-world data, and/or synthetic data that represent sensordata representations (e.g., images, LiDAR point clouds, etc.) includingvarying weather, lighting, and/or other conditions. Each sensor datainstance may include corresponding ground truth data representative of avisibility distance and/or a visibility distance bin. In someembodiments, the ground truth data may be generated automatically usingone or more trained models, such that for given parameters - e.g., fogdensity, rain wetness, rain intensity, etc. - there is a known orestimated visibility distance. As such, a robust training set may begenerated using the one or more models (e.g., different models maycorrespond to different sensor data types, such as real-world,augmented, and/or synthetic), and the machine learning model may betrained using the combination of the training data and the associatedground truth data.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for deep neural network processing forvisibility distance estimation in autonomous machine applications aredescribed in detail below with reference to the attached drawingfigures, wherein:

FIG. 1 is a data flow diagram illustrating a process for training amachine learning model to compute estimated visibility distances, inaccordance with some embodiments of the present disclosure;

FIGS. 2A-2C are example visualizations of sensor data with differentvisibility distances, in accordance with some embodiments of the presentdisclosure;

FIG. 3 is a flow diagram illustrating a method for training a machinelearning model to compute estimated visibility distances, in accordancewith some embodiments of the present disclosure;

FIG. 4 is a data flow diagram illustrating a process for deploying amachine learning model to compute estimated visibility distances, inaccordance with some embodiments of the present disclosure;

FIG. 5 is an example visualization of sensor data and correspondingvisibility distance outputs for various objects, in accordance with someembodiments of the present disclosure;

FIG. 6 is a flow diagram illustrating a method for deploying a machinelearning model to compute estimated visibility distances, in accordancewith some embodiments of the present disclosure;

FIG. 7A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 7B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 7A, in accordance with someembodiments of the present disclosure;

FIG. 7C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 7A, in accordance with someembodiments of the present disclosure;

FIG. 7D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 7A, in accordancewith some embodiments of the present disclosure;

FIG. 8 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure; and

FIG. 9 is a block diagram of an example data center suitable for use inimplementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to deep neural networkprocessing for visibility distance estimation in autonomous machineapplications. Although the present disclosure may be described withrespect to an example autonomous vehicle 700 (alternatively referred toherein as “vehicle 700” or “ego-vehicle 700,” an example of which isdescribed with respect to FIGS. 7A-7D), this is not intended to belimiting. For example, the systems and methods described herein may beused by, without limitation, non-autonomous vehicles, semi-autonomousvehicles (e.g., in one or more advanced driver assistance systems(ADAS)), piloted and un-piloted robots or robotic platforms, warehousevehicles, off-road vehicles, vehicles coupled to one or more trailers,flying vessels, boats, shuttles, emergency response vehicles,motorcycles, electric or motorized bicycles, aircraft, constructionvehicles, underwater craft, drones, and/or other vehicle types. Inaddition, although the present disclosure may be described with respectto visibility distance estimation in autonomous or semi-autonomousmachine applications, this is not intended to be limiting, and thesystems and methods described herein may be used in augmented reality,virtual reality, mixed reality, robotics, security and surveillance,autonomous or semi-autonomous machine applications, and/or any othertechnology spaces where the condition and usability of sensor data maybe analyzed.

Training a Machine Learning Model to Compute Visibility Distances

With reference to FIG. 1 , FIG. 1 is a data flow diagram correspondingto an example process 100 for training a machine learning model forvisibility distance estimations, in accordance with some embodiments ofthe present disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. In some embodiments, the systems,methods, and processes described herein may be executed using similarcomponents, features, and/or functionality to those of exampleautonomous vehicle 700 of FIGS. 7A-7D, example computing device 800 ofFIG. 8 , and/or example data center 900 of FIG. 9 .

The machine learning model(s) 104 may be trained using training data102 - such as sensor data 102A, augmented data 102B, and/or syntheticdata 102C. For example, the sensor data 102A may correspond toreal-world sensor data generated using one or more sensors of theego-machine 700, such as stereo cameras 768, RADAR sensors 760,ultrasonic sensors 762, LiDAR sensors 764, wide-view cameras 770,surround cameras 774, other sensors of the ego-machine 700, and/or othersensor types. For example, the sensor data 102A may be collected usingone or more data collection vehicles that collect various types ofsensor data 102A in various conditions - such as varying weather,lighting, occlusion, and/or other conditions. In embodiments, becausegenerating a diverse enough training data set may be impractical and/orprohibitively costly using sensor data 102A alone, augmented data 102Band/or synthetic data 102C may be generated in addition to oralternatively from the real-world sensor data 102A.

The augmented data 102B may correspond to real-world sensor data 102Athat is augmented - e.g., to include various weather (fog, snow, rain,sleet, etc.), lighting (darkness, sunny, sun shining towards sensor,etc.), occlusion, and/or other conditions - to simulate sensor datacaptured in various conditions other than the actual conditions thesensor data 102A was originally captured. For example, where sensor data102A is captured under fair weather conditions, the sensor data 102A maybe augmented using rain, fog, snow, and/or another condition to generatethe augmented sensor data 102B. As another example, where the sensordata 102A is captured under light rain, the sensor data 102A may beaugmented to include heavier rain and/or fog in addition to the lightrain. To do this, in embodiments, different levels of rain (e.g.,drizzle, heavy rain, etc.), fog (e.g., dense fog, light fog, etc.), snow(e.g., heavy snow, light snow, flurry, etc.), and/or other conditions(e.g., different sun locations to create different lighting conditionsfor the sensor(s)) may be applied to generate the augmented data 102B.As such, values corresponding to parameters that control one or moreconditions may be determined - e.g., manually, automatically, and/orrandomly - to generate the augmented data 102B. For example, values fora fog density parameter, a rain intensity parameter, and/or anotherparameter for another condition may be determined, and the sensor data102A may be augmented based on the values to generate the augmented data102B.

The synthetic data 102C may correspond to sensor data generated usingone or more virtual sensors (e.g., cameras, LiDAR sensors, RADARsensors, etc.) of one or more virtual machines (e.g., a virtual instanceof the vehicle 700) within a virtual environment (e.g., a virtual cameraof a virtual vehicle within a virtual or simulated environment). Forexample, a simulation or game engine may be used to generate simulatedenvironments for the virtual machine, and the virtual sensors maygenerate synthetic data 102C from within the simulated environment foruse as training data 102. To generate simulated or virtual environmentsthat correspond to various conditions - e.g., weather, lighting,occlusion, etc. - values for parameters corresponding to variousconditions may be set manually, automatically, and/or randomly togenerate a diversity of the synthetic data 102C. In addition, roadlayouts (e.g., number of lanes, curvature, elevation change, etc.),traffic conditions, surrounding environment conditions, scenery,locations and/or types of objects, and/or other factors in the virtualenvironment may be selected or randomized to further diversify thetraining data 102 using the synthetic data 102C.

In order to calibrate the augmented data 102B and the synthetic data102C to ensure coherency between the two, and thus more accuratetraining data 102, the augmentation and simulation parameters may becalibrated. This may increase the likelihood that data generated usingaugmentation and simulation with a same visibility distance label arevisually the same or similar. To perform the calibration, inembodiments, an instance of augmented data 102B and an instance ofsynthetic data 102C may be curated that include an object at a samedistance that corresponds to the visibility distance. For each instance,a pair of augmented instances and a pair of synthetic instances may begenerated at the distance of (for example and without limitation) +2meters and at the distance of (for example and without limitation) -2meters. An assessor may then determine whether the target object isvisible in both instances of the augmented data 102B and both instancesof the synthetic data 102C. According to embodiments under such examplescenarios, the parameters may then be tuned until the object is visibleat distance -2 meters and is not visible at the distance +2 meters. Assuch, for a same visibility distance, the parameters for rain, snow,fog, etc. may be tuned such that the resulting training data forsynthetic or augmented data is visually similar.

In embodiments, the training data 102 may include original data 102A,102B, and/or 102C, down-sampled data, up-sampled data, cropped or regionof interest (ROI) data, flipped or rotated data, otherwise augmenteddata, and/or a combination thereof in order to further diversify thetraining data set and to increase the robustness of the training dataset.

The training data 102 - in addition to corresponding ground truth datagenerated using ground truth generator 116 - may be used to train themachine learning model(s) 104 to compute outputs 106 (e.g., visibilitydistances 108, distance bins 110, and/or visibility classificationsand/or attributes 112). Although examples are described herein withrespect to using deep neural networks (DNNs), and specificallyconvolutional neural networks (CNNs), as the machine learning model(s)104, this is not intended to be limiting. For example, and withoutlimitation, the machine learning model(s) 104 may include any type ofmachine learning model, such as a machine learning model(s) using linearregression, logistic regression, decision trees, support vector machines(SVM), Naive Bayes, k-nearest neighbor (Knn), K means clustering, randomforest, dimensionality reduction algorithms, gradient boostingalgorithms, neural networks (e.g., auto-encoders, convolutional,recurrent, perceptrons, long/short term memory/LSTM, Hopfield,Boltzmann, deep belief, deconvolutional, generative adversarial, liquidstate machine, etc.), computer vision algorithms, and/or other types ofmachine learning models.

As an example, such as where the machine learning model(s) 104 includesa CNN, the machine learning model(s) 104 may include any number oflayers. One or more of the layers may include an input layer. The inputlayer may hold values associated with the training data 102 (or sensordata 402, in deployment) (e.g., before or after post-processing). Forexample, when the training data represents an image, the input layer mayhold values representative of the raw pixel values of the image(s) as avolume (e.g., a width, a height, and color channels (e.g., RGB), such as32 x 32 x 3).

One or more layers may include convolutional layers. The convolutionallayers may compute the output of neurons that are connected to localregions in an input layer, each neuron computing a dot product betweentheir weights and a small region they are connected to in the inputvolume. A result of the convolutional layers may be another volume, withone of the dimensions based on the number of filters applied (e.g., thewidth, the height, and the number of filters, such as 32 x 32 x 12, if12 were the number of filters).

One or more layers may include deconvolutional layers (or transposedconvolutional layers). For example, a result of the deconvolutionallayers may be another volume, with a higher dimensionality than theinput dimensionality of data received at the deconvolutional layer.

One or more of the layers may include a rectified linear unit (ReLU)layer. The ReLU layer(s) may apply an elementwise activation function,such as the max (0, x), thresholding at zero, for example. The resultingvolume of a ReLU layer may be the same as the volume of the input of theReLU layer.

One or more of the layers may include a pooling layer. The pooling layermay perform a down sampling operation along the spatial dimensions(e.g., the height and the width), which may result in a smaller volumethan the input of the pooling layer (e.g., 16 x 16 x 12 from the 32 x 32x 12 input volume).

One or more of the layers may include one or more fully connectedlayer(s). Each neuron in the fully connected layer(s) may be connectedto each of the neurons in the previous volume. The fully connected layermay compute class scores, and the resulting volume may be 1 x 1 x n,where n is equivalent to the number of classes. In some examples, theCNN may include a fully connected layer(s) such that the output of oneor more of the layers of the CNN may be provided as input to a fullyconnected layer(s) of the CNN. In some examples, one or moreconvolutional streams may be implemented by the machine learningmodel(s) 104, and some or all of the convolutional streams may include arespective fully connected layer(s).

In some non-limiting embodiments, the machine learning model(s) 104 mayinclude a series of convolutional and max pooling layers to facilitateimage feature extraction, followed by multi-scale dilated convolutionaland up-sampling layers to facilitate global context feature extraction.

Although input layers, convolutional layers, pooling layers, ReLUlayers, and fully connected layers are discussed herein with respect tothe machine learning model(s) 104, this is not intended to be limiting.For example, additional or alternative layers may be used in the machinelearning model(s) 104, such as normalization layers, SoftMax layers,and/or other layer types.

In addition, some of the layers may include parameters (e.g., weightsand/or biases), such as the convolutional layers and the fully connectedlayers, while others may not, such as the ReLU layers and poolinglayers. In some examples, the parameters may be learned by the machinelearning model(s) 104 during training. Further, some of the layers mayinclude additional hyper-parameters (e.g., learning rate, stride,epochs, etc.), such as the convolutional layers, the fully connectedlayers, and the pooling layers, while other layers may not, such as theReLU layers. In embodiments where the machine learning model(s) 104regress on visibility distances, the activation function of a last layerof the CNN may include a ReLU activation function. In embodiments wherethe machine learning model(s) 104 classifies instances of sensor datainto a distance bin 110, the activation function of a last layer of theCNN may include a SoftMax activation function. The parameters andhyper-parameters are not to be limited and may differ depending on theembodiment.

In embodiments where the machine learning model(s) 104 includes a CNN,different orders and numbers of the layers of the CNN may be useddepending on the embodiment. In other words, the order and number oflayers of the CNN is not limited to any one architecture.

For example, in one or more embodiments, the CNN may include an encoderdecoder architecture, and/or may include one or more output heads. Forexample, the CNN may include one or more layers corresponding to afeature detection trunk of the CNN, and the outputs of the featuredetection trunk (e.g., feature maps) may be processed using one or moreoutput heads. For example, a first output head (including one or morefirst layers) may be used to compute the visibility distance 108, asecond output head (including one or more second layers) may be used tocompute the distance bin(s) 110, and/or third output head(s) (includingone or more third layers) may be used to compute the visibilityclassifications/attributes 112. As such, where two or more heads areused, the two or more heads may process data from the trunk in parallel,and each head may be trained to accurately predict the correspondingoutput(s) of that output head. In other embodiments, however, a singletrunk may be used, without separate heads.

The ground truth generator 116 may be used to generate real-world labelsor annotations 118 as ground truth data corresponding to the real-worldsensor data 102A. For example, for the sensor data 102A generated in areal-world environment - without augmentation - the real-world labels orannotations may correspond to visibility distance labels indicating thevisibility distance for each instance of the sensor data 102A. Where thereal-world labels or annotations 118 are used to generate the groundtruth data, the annotations or labels may be generated within a drawingprogram (e.g., an annotation program), a computer aided design (CAD)program, a labeling program, another type of program suitable forgenerating the annotations, and/or may be hand drawn, in some examples.In any example, the ground truth data may be synthetically produced(e.g., generated from computer models or renderings), real produced(e.g., designed and produced from real-world data), machine-automated(e.g., using feature analysis and learning to extract features from dataand then generate labels), human annotated (e.g., labeler, or annotationexpert, defines the location of the labels), and/or a combinationthereof (e.g., human identifies an object that is visible, computerdetermines distance to object and corresponding visibility distance).

The real-world labels 118 may correspond to an actual visibilitydistance - e.g., in meters, feet, etc. - and/or may correspond todistance bins (e.g., each distance bin may include a range of visibilitydistance values). For example, where distance bins are used, and withoutlimitation, the distance bins may include a first bin for very lowvisibility (e.g., <10 meters), a second bin for low visibility (e.g.,between 10 meters and 100 meters), a third bin for medium visibility(e.g., between 100 meters and 1000 meters), a fourth bin for highvisibility (e.g., between 1000 meters and 4000 meters), and a fifth binfor clear visibility (e.g., greater than 4000 meters). Although fivebins are listed here, with corresponding ranges, this is not intended tobe limiting, and number of distance bins with any ranges of values maybe used without departing from the scope of the present disclosure. Assuch, for a given instance of the sensor data 102A, the sensor data 102Amay be labeled with a visibility distance and/or a distance bin, andthese values may be used to compare - e.g., using loss function(s) 114 -to the outputs 106 of the machine learning model(s) 104 during training.In some instances, the machine learning model(s) 104 may also be trainedto compute visibility classifications and/or attributes 112 - e.g.,corresponding to a cause of the reduced visibility, where present. Inother instances, in addition to or alternative from visibility distanceclassifications, the machine learning model(s) 104 may be trained tocompute sensor blindness classifications that correspond to impairedsensor data (e.g., rain drops on a camera lens, snow blocking thesensor, etc.) In such instances, the ground truth data and outputs maybe similar to those described in U.S. Non-Provisional Application No.16/570,187, filed on Sep. 13, 2019, which is hereby incorporated byreference in its entirety.

To determine the visibility distance and/or the distance bin for a giveninstance of the sensor data 102A, static and/or dynamic object locationsmay be used. For example, in some instances, one or more depth ordistance values may be determined for objects in the environment usingone or more machine learning, computer vision, depth sensor, and/orother outputs, and these depth or distance values may be used todetermine the visibility distance information for the instance of sensordata 102A. With respect to FIG. 2A, assuming visualization 200A(including a heavy rain) corresponds to an instance of the sensor data102A (e.g., an image from a camera of a data collection vehicle), adepth value(s) may be known for vehicle 202A based on an output from adepth sensor (e.g., LiDAR, RADAR, etc.), an output from a machinelearning model or neural network, an output from a computer visionalgorithms, and/or another output type. In such an example, as the datacollection vehicle was generating the instance of the sensor data 102Aincluding the vehicle 202A, the data collection vehicle may have beenrunning one or more underlying processes for determining depth ordistance information to objects in the environment. As such, if thevehicle 202A was the furthest visible object from the data collectionvehicle, and the depth to the vehicle 202A is known, the visibilitydistance for the instance of the sensor data 102A may correspond to thedepth or distance to the vehicle 202A (e.g., plus an addition distancewhere the environment is visible beyond the vehicle 202A, or less somedistance where the vehicle 202A is slightly visible, or blurred, inembodiments). Similarly, for visualization 200B (including light rainconditions), vehicle 202B may be visible, and thus the visibilitydistance and/or distance bin may be determined using depth or distancevalues corresponding to the vehicle 202B.

In some embodiments, in addition to or alternatively from using depthoutputs from sensors, machine learning models, computer visionalgorithms, etc., a high definition (HD) map may be used to determinedistances or depths to static objects in an environment. For example,using localization techniques to localize the data collection vehiclewith respect to the HD map, identifiable or visible objects in theenvironment with known distances from a localized location of the datacollection vehicle may be used to determine the visibility distance fora given instance of the sensor data 102A. As such, where, for example, atraffic sign, tree, streetlight, intersection, building, and/or staticfeature is visible in the instance of the sensor data 102A, and thedistance to the object or feature is known from the HD map afterlocalization, the distance or depth may be used as the visibilitydistance and/or to determine the distance bin.

In some embodiments, this determination of the ground truth label forvisibility distance and/or distance bin may be performed manually. Forexample, a human annotator may determine a furthest object that isvisible in an instance of sensor data 102A, determine the associateddistance or depth value for that object, and generate the ground truthaccordingly. In other embodiments, the determination of the ground truthlabel for visibility distance and/or distance bin may be executedautomatically, such that a depth or distance value for an objectidentified using a depth sensor output, machine learning model, DNN,computer vision algorithm, HD map, etc. that is greatest (e.g., that isthe furthest distance from the data collection vehicle) may be used asthe visibility distance value and/or may be used to determine thedistance bin for the instance of the sensor data 102A.

To generate the ground truth data for the augmented data 102B, a similarprocess may be used as for the sensor data 102A, in embodiments. Forexample, known distance or depth values for the objects in theenvironment may be used to determine the visibility distance and/ordistance bins. In addition or alternatively, a model may be trained todetermine a correspondence or mapping between values for parameters ofthe augmentation and the visibility distance and/or distance bins. Forexample, values for fog parameters (e.g., density, height, etc.) may beused to augment the sensor data 102A to generate augmented data 102B.The instance of the augmented data 102B may then be analyzed (e.g.,using known depth values for visible objects after augmentation) todetermine the visibility distance and/or distance bins. This process maybe repeated for any number of instances of the augmented data 102B untilthe model is trained to compute visibility distances and/or distancebins based on values of the parameters for augmentation. Once the modelis trained, this model may be used to automatically generate groundtruth for the automatically generated augmented data. For example, thevalues for the parameters (e.g., for rain, snow, fog, sleet, lighting,occlusion, etc.) may be randomized to generate the augmented data 102B,and the values (or combinations thereof) may have known correspondenceto visibility distance and/or distance bins, and these visibilitydistances and/or distance bins may be used as ground truth for theinstances of augmented data 102B.

As an example of training a model, or creating a mapping between valuesof parameters and visibility distances and/or distance bins, and withrespect to FIGS. 2A-2C, the visualization 200A may correspond to valuesfor rain parameters that are used to generate augmented data 102B with aheavy rain. An annotator or labeler may then determine a distance to thevehicle 202A, label the augmented data 102B as such, and this maycorrespond to one mapping between values of one or more parameters foraugmenting the data and visibility distances and/or distance bins. Thisprocess may be repeated for visualization 200B using the distance to thevehicle 202B and the values for the parameters that result in a lightrain, and for visualization 200C using the distance to the vehicle 202Cand the values for the parameters that result in a clear condition(e.g., all rain values at 0 as there is no rain). Although rain isillustrated and described with respect to FIGS. 2A-2C, this is notintended to be limiting, and other conditions such as fog, snow, sleet,hail, lighting, occlusion, a combination thereof, etc. may be used togenerate the augmented data 102B, and thus the mapping between values ofparameters (or combinations thereof) and visibility distances and/ordistance bins for ground truth.

To generate the ground truth data for the synthetic data 102C, similarprocesses may be used as with the sensor data 102A, except the knowndepth information may come from the simulation engine as state datacorresponding to the simulation may include accurate depth or distanceinformation for objects in the environment. For example, a humanannotator may identify a furthest visible object in an instance of thesynthetic data 102C generated according to varying values for parametersin the simulation, and a depth or distance from the virtual sensor ofthe virtual machine to the furthest visible object may be used as thevisibility distance and/or to determine distance bin as ground truth. Inaddition or alternatively, in embodiments, a model may be trained todetermine a correspondence or mapping between values for parameters ofthe simulation and the visibility distance and/or distance bins. Forexample, values for fog parameters (e.g., density, height, etc.) may beused to generate the simulated environment of the simulation, and thesynthetic data 102C may be captured from within the simulatedenvironment. The instance of the synthetic data 102C may then beanalyzed (e.g., using known depth values for visible objects in thesimulation) to determine the visibility distance and/or distance bins.This process may be repeated for any number of instances of thesynthetic data 102C until the model is trained to compute visibilitydistances and/or distance bins based on values of the parameters forsimulation. In some embodiments, a tool may be used to allow a user toidentify when a particular object is visible or is not visible for acertain set of parameters. For example, a vehicle may be placed at afirst location, and the user may indicate that the vehicle is visible.The vehicle may then be moved further away, and the user may indicatethe vehicle is still visible. The vehicle may then be moved even furtheraway in the simulated environment and the user may indicate that thevehicle is no longer visible, and this distance of the vehicle may beused as the ground truth visibility distance and/or to determine thedistance bin. This process may be repeated with various parameters andfor various object types, until the model is trained. Once the model istrained, this model may be used to automatically generate ground truthfor the automatically generated synthetic data. For example, the valuesfor the parameters (e.g., for rain, snow, fog, sleet, lighting,occlusion, etc.) may be randomized to generate the simulations, and thevalues (or combinations thereof) may have known correspondence tovisibility distance and/or distance bins, and these visibility distancesand/or distance bins may be used as ground truth for the instances ofsynthetic data 102C.

As an example of training a model, or creating a mapping between valuesof parameters and visibility distances and/or distance bins, and withrespect to FIGS. 2A-2C, the visualization 200A may correspond to valuesfor rain parameters that are used to generate synthetic data 102C with aheavy rain. An annotator or labeler may then determine a distance to thevehicle 202A, label the synthetic data 102C as such, and this maycorrespond to one mapping between values of one or more parameters forgenerating a simulation and visibility distances and/or distance bins.This process may be repeated for visualization 200B using the distanceto the vehicle 202B and the values for the parameters that result in alight rain, and for visualization 200C using the distance to the vehicle202C and the values for the parameters that result in a clear condition(e.g., all rain values at 0 as there is no rain). Although rain isillustrated and described with respect to FIGS. 2A-2C, this is notintended to be limiting, and other conditions such as fog, snow, sleet,hail, lighting, occlusion, a combination thereof, etc. may be used togenerate the synthetic data 102C, and thus the mapping between values ofparameters (or combinations thereof) and visibility distances and/ordistance bins for ground truth.

Once the ground truth data is generated - using the ground truthgenerator 116 - to correspond to the outputs 106 of the machine learningmodel(s) 104 as computed using the training data 102, one or more lossfunctions 114 may be used to determine an accuracy of the machinelearning model(s) 104, and to update (e.g., parameters, such as weightsand biases) the machine learning model(s) 104 at each iteration until anacceptable level of accuracy is reached. Where the machine learningmodel(s) 104 is trained to regress on a visibility distance value,regression loss may be used as a loss function 114. In such an example,the regression loss may include normalized L1 loss, which may accountfor higher error at further visibility distances and less error atshorter visibility distances. In such an example, the loss may becomputed according to equation (1) below:

$Regression\, Loss = \frac{d_{pred} - d_{GT}}{d_{GT}}$

where d_(pred) is the predicted visibility distance output by themachine learning model(s) 104 and d_(GT)is the ground truth visibilitydistance.

In other examples, a normalized L2 loss may be used, or a directionalnormalized L1 loss may be used. Directional normalized L1 loss may besimilar to normalized L1 loss, but the slope of the loss curve may besteeper on the positive side to incentivize errors to the positive side(e.g., to vapor recall over precision at the loss level). The minimumvalue for this loss function may still be at 0, but predictions lowerthan ground truth may be penalized more.

In still further examples, a Gaussian distance loss may be used for aregression channel in order to penalize distances that are further fromthe ground truth more, and predictions that are closer to the groundtruth less (rather than requiring be completely accurate). In such anexample, overestimation of the distance may be penalized more thanunderestimation.

In embodiments where the machine learning model(s) 104 outputs thedistance bins 110 (e.g., as a classification output), a classificationloss may be used. For example, to train the machine learning model(s)104 to compute the distance bin 110, a categorical cross-entropy lossfunction may be used and/or a directional categorical cross-entropy lossfunction may be used.

During training of the machine learning model(s) 104, and to determinean acceptable level of accuracy, one or more key performance indicators(KPIs) may be used. For example, a network (or machine learning model)level KPI may be used and a module level KPI may be used. The networklevel KPI may measure relative visibility distance prediction error(RDE) for each instance of training data 102 according to equation (2)below:

$RDE = \frac{d_{pred} - d_{GT}}{d_{GT}}$

where d_(pred) is the predicted visibility distance output by themachine learning model(s) 104 and d_(GT)is the ground truth visibilitydistance. Using these values for each instance of training data 102, themean and standard deviation may be analyzed.

The module level KPI may measure error in classification of predictedvisibility distance into various bins. To compute the error inclassification (Δ_(class)), equation (3), below, may be used:

Δ_(class)= c_(pred) − c_(GT)

where c is the distance bin identifier (ID) (e.g., 1 corresponding tovery low, 5 corresponding to clear), ^(C)pred is the visibility distancebin ID predicted by the machine learning model(s) 104 and C_(GT)is theground truth visibility bin ID. As such, Δ_(class) may be 0 for correctpredictions, positive if the predicted distance bin is higher thanexpected, and negative if the predicted distance bin is lower thanexpected. In this way, Δ_(class) = 0 may be a true positive, Δ_(class) >0 as a false negative, and Δ_(class) < 0 as a false positive. This maybe to account for false positives causing the ego-machine 700 to beover-conservative (which may be uncomfortable for an occupant) and afalse negative to cause the ego-machine 700 to be under-conservative(which may be dangerous for an occupant). Using these definitions oftrue positive, false positive, and false negative, the module may betuned to achieve good precision and recall, but may favor recall ifneeded (e.g., may favor being over-conservative). In addition, the|Δ_(class)| may be used as indicator of the predicted error is, and theloss function may be used to reduce the spread of |Δ_(c)l_(ass)|.

Now referring to FIG. 3 , each block of method 300, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 300 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 300 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,method 300 is described, by way of example, with respect to the process100 of FIG. 1 and the ego-machine 700 of FIGS. 7A-7D. However, thismethod 300 may additionally or alternatively be executed within any oneprocess and/or by any one system, or any combination of processes andsystems, including, but not limited to, those described herein.

FIG. 3 is a flow diagram illustrating a method 300 for training amachine learning model to compute estimated visibility distances, inaccordance with some embodiments of the present disclosure. The method300, at block B302, includes receiving values corresponding to one ormore parameters for adjusting visibility corresponding to an instance oftraining sensor data. For example, values for one or more parameters(e.g., rain intensity, rain wetness, fog density, lighting conditions,etc.) may be received.

The method 300, at block B304, includes generating the instance oftraining sensor data based at least in part on the values. For example,using the values of the one or more parameters, the sensor data 102A maybe augmented to generate the augmented data 102B and/or a virtualsimulation may be generated, and the synthetic data 102C may be capturedusing a virtual sensor of a virtual machine.

The method 300, at block B306, includes determining a visibilitydistance corresponding to the instance of training sensor data based atleast in part on the values. For example, using one or more trainedmodels, a correspondence between the one or more values of the one ormore parameters and a visibility distance 108 and/or a visibilitydistance bin 110 may be determined.

The method 300, at block B308, includes training a machine learningmodel using the instance of training sensor data and the visibilitydistance as ground truth data. For example, the machine learningmodel(s) 104 may be trained using one or more loss function(s) 114 tocompare the outputs 106 to the ground truth data (e.g., corresponding toground truth visibility distances, ground truth distance bins, and/orground truth visibility classifications/attributes) generated using theground truth generator 116. Any number (e.g., thousands, millions, etc.)of instances of training data 102 may be used to train the machinelearning model(s) 104 until the machine learning model(s) 104 reaches anacceptable level of accuracy (as determined using one or more KPIs, inembodiments) and is validated for deployment (e.g., in process 400 ofFIG. 4 ).

Computing Visibility Distances in Deployment

With reference to FIG. 4 , FIG. 4 is a data flow diagram correspondingto an example process 400 for deploying a machine learning model forvisibility distance estimations, in accordance with some embodiments ofthe present disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. In some embodiments, the systems,methods, and processes described herein may be executed using similarcomponents, features, and/or functionality to those of exampleautonomous vehicle 700 of FIGS. 7A-7D, example computing device 800 ofFIG. 8 , and/or example data center 900 of FIG. 9 .

In deployment, the process 400 may include generating and/or receivingsensor data 402. The sensor data 402 may include sensor data generatedusing one or more sensors of an ego-machine - such as the ego-machine700 described herein with respect to FIGS. 7A-7D. For example, thesensor data 402 may be similar to the sensor data 102A described withrespect to FIG. 1 . As such, the sensor data 402 may represent a fieldof view and/or a sensory field of one or more sensors of the ego-machine700 - such as in the form of an image, a LiDAR range image, a pointcloud, etc.

The machine learning model(s) 104 may receive the sensor data 402 asinput, and may process the sensor data 402 to compute the output(s)106 - which may include the visibility distance 108, the distance bin(s)110, and/or the visibility classifications/attributes 112. Inembodiments where the machine learning model(s) 104 computes thevisibility distance 108 (and not the distance bin(s) 110), apost-processor 404 may be used to threshold the visibility distance 108into a distance bin. For example, where a distance bin is between 10meters and 100 meters, and the visibility distance 108 is 78 meters, thepost-processor 404 may label or classify the instance of sensor data 402as corresponding to the distance bin between 10 meters and 100 meters,and this information may be analyzed for usability 406.

In any example, whether the distance bin(s) 110 are computed directly asan output 106 of the machine learning model(s) 104 or determined usingthe post-processor 404, there may be any number of distance bins, andeach distance bin may include any range of visibility distance values.In addition, each bin may correspond to a different number of operationsthat may be performed. For example, where the sensor data 402 is notvisually impaired in any way, there may be a set of operations that theego-machine 700 may rely on the sensor data 402 for. However, where thesensor data 402 is visually impaired in some way (e.g., the visibilitydistance is less than a maximum), one or more operations of the set ofoperations may be disabled, or an indicator may be provided that causesone or more components, features, and/or functionalities that rely onthe sensor data 402 to ignore the instances of the sensor data 402 thatare not suitable or usable. In addition, in embodiments, where thevisibility classifications/attributes 112 are computed, this informationmay additionally be used as a factor in the usability 406 decision, suchthat certain visibility classifications in combination with certaindistance bins may correspond to different usability than othervisibility classifications in combination with other distance bins.

As a non-limiting example, the distance bins may include a first bin forvery low visibility (e.g., <10 meters), a second bin for low visibility(e.g., between 10 meters and 100 meters), a third bin for mediumvisibility (e.g., between 100 meters and 1000 meters), a fourth bin forhigh visibility (e.g., between 1000 meters and 4000 meters), and a fifthbin for clear visibility (e.g., greater than 4000 meters). The firstbin, in this example, may correspond to a visual range correlated toextreme fog or blowing snow, or high precipitation or heavy drizzle. Thesecond bin may correspond to a visual range correlated to dense fogweather. The third bin may correspond to a visual range correlated tomoderate fog weather or medium precipitation or moderate drizzle. Thefourth bin may correspond to a visual range correlated to a hazy weathercondition or light precipitation or drizzle. The fifth bin maycorrespond to a visual range correlated to a maximum visual range of theparticular sensor(s) generating the sensor data 402.

Continuing with this non-limiting example, each bin of the five bins maycorrespond to various tasks that may be performed by the ego-machine.For example, for the first bin (e.g., very low visibility distance), thefull performance of Level 0, Level 1, and Level 2 low speed activesafety functions (e.g., lane assist, automatic lane keep, automaticcruise control, automatic emergency braking, etc.) may be available solong as the ego-machine is traveling at less than a threshold speed(e.g., less than 25 kilometers per hour). However, if the vehicle istraveling beyond the threshold speed, these functionalities may bedisabled - at least with respect to the instance(s) of sensor data 402with a very low disability distance. With respect to the second bin(e.g., low visibility distance), the full performance of the Level 0,Level 1, and Level 2 parking functions (e.g., proximity detection,automatic parallel parking alignment, etc.) and low speed active safetyfunctions may be available so long as the ego-machine is traveling atless than a threshold speed. With respect to the third bin (e.g., mediumvisibility distance), the full performance of the Level 0, Level 1,Level 2 and Level 2+ driving functions may be available. With respect tothe fourth bin (e.g., high visibility distance), the full performance ofLevel 3 and Level 4 parking functions (e.g., automatic parallel parking(APP), MPP, VVP) may be available, in addition to the Level 0, Level 1,Level 2, and Level 2+ functions of the first, second, and third bin.With respect to the fourth bin (e.g., clear visibility), the fullperformance of Level 3 automated driving highway function may beavailable, in addition to the functionality of the first, second, third,and fourth bins.

In some embodiments, the usability 406 may correspond to a portion ofthe information from the sensor data 402 that may be used. For example,where the distance bin is known, and the distance bin corresponds to arange of 100 to 1000 meters, any outputs of the system that correspondto the sensor data 402 and that are within 1000 meters of theego-machine 700 may be used, while any outputs that correspond to adistance greater than 1000 meters may be ignored. In such an example,where a first vehicle is detected at a distance of 200 meters from theego-machine 700 using an object detection algorithm, the detection maybe relied upon by downstream systems of the drive stack 408. However,where a second vehicle is detected at a distance of 1200 meters from theego-machine 700 using the object detection algorithm the detection maynot be relied upon. This similar process may be used for other taskssuch as object tracking, obstacle in path analysis (OIPA), object tolane assignment, localization, etc.

In any embodiment, once the usability 406 is determined, the instance ofsensor data 402 may be tagged with or transmitted to an autonomous orsemi-autonomous software driving stack (“drive stack”) with anindication of the usability 406, such that one or more features,functionalities, and/or components of the drive stack 408 may either bedisabled, ignore the instance of the sensor data 402, and/or otherwiseuse the usability 406. The drive stack 408 may include one or morelayers, such as a world state manager layer that manages the world stateusing one or more maps (e.g., 3D maps), localization component(s),perception component(s), and/or the like. In addition, the autonomousdriving software stack may include planning component(s) (e.g., as partof a planning layer), control component(s) (e.g., as part of a controllayer), actuation component(s) (e.g., as part of an actuation layer),obstacle avoidance component(s) (e.g., as part of an obstacle avoidancelayer), and/or other component(s) (e.g., as part of one or moreadditional or alternative layers). As such, the usability 406 mayprovide an indication to any of the downstream tasks of the drive stack408 that rely on the sensor data 402 such that appropriate use of thesensor data 402 is managed.

As an example, and with respect to visualization 500 of FIG. 5 , aninstance of sensor data 402 representative of an image may be generatedusing a camera of the ego-machine 700. The instance of sensor data 402may be applied to the machine learning model(s) 104, and the machinelearning model(s) 104 may compute one or more of the outputs 106. Wherethe output 106 includes the visibility distance 108, the post-processor404 may threshold the visibility distance 108 into a distance bin. Theusability 406 may then be determined based on the distance bin (and/orthe visibility classification/attributes 112), and this information maybe used by the drive stack 408. For example, where the distance bincorresponds to very low visibility, safety functions such as automaticemergency braking (AEB) may be used to aid the ego-machine 700 instopping for the vehicle 502A, but an object detection resultcorresponding to the vehicle 502B may not be relied upon due to thereduced usability 406 of the sensor data 402 corresponding to thevisualization 500.

Now referring to FIG. 6 , each block of method 600, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 600 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 600 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,method 600 is described, by way of example, with respect to the process400 of FIG. 4 and the ego-machine 700 of FIGS. 7A-7D. However, thismethod 600 may additionally or alternatively be executed within any oneprocess and/or by any one system, or any combination of processes andsystems, including, but not limited to, those described herein.

FIG. 6 is a flow diagram illustrating a method 600 for deploying amachine learning model to compute estimated visibility distances, inaccordance with some embodiments of the present disclosure. The method600, at block B602, includes computing, using a machine learning modeland based at least in part on sensor data generated using one or moresensors of an ego-machine, data indicative of a visibility distancecorresponding to the sensor data. For example, the machine learningmodel(s) 104 may compute one or more of the outputs 106 using the sensordata 402 as input.

The method 600, at block B604, includes determining, based at least inpart on the visibility distance, a usability of the sensor data for oneor more operations of the ego-machine. For example, the usability 406 ofthe sensor data 402 may be determined based on the visibility distance108, the distance bin 110, and/or the visibilityclassification/attributes 112.

The method 600, at block B606, includes performing at least oneoperation of the one or more operations based at least on part on theusability of the sensor data. For example, where the visibility distanceis not clear, one or more operations that otherwise would rely on thesensor data 402 were the sensor data clear may be deactivated, and/ormay be signaled to ignore the particular instance of sensor data 402.

Example Autonomous Vehicle

FIG. 7A is an illustration of an example autonomous vehicle 700, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 700 (alternatively referred to herein as the “vehicle700”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,a vehicle coupled to a trailer, and/or another type of vehicle (e.g.,that is unmanned and/or that accommodates one or more passengers).Autonomous vehicles are generally described in terms of automationlevels, defined by the National Highway Traffic Safety Administration(NHTSA), a division of the US Department of Transportation, and theSociety of Automotive Engineers (SAE) “Taxonomy and Definitions forTerms Related to Driving Automation Systems for On-Road Motor Vehicles”(Standard No. J3016-201806, published on Jun. 15, 2018, Standard No.J3016-201609, published on Sep. 30, 2016, and previous and futureversions of this standard). The vehicle 700 may be capable offunctionality in accordance with one or more of Level 3 - Level 5 of theautonomous driving levels. The vehicle 700 may be capable offunctionality in accordance with one or more of Level 1 - Level 5 of theautonomous driving levels. For example, the vehicle 700 may be capableof driver assistance (Level 1), partial automation (Level 2),conditional automation (Level 3), high automation (Level 4), and/or fullautomation (Level 5), depending on the embodiment. The term“autonomous,” as used herein, may include any and/or all types ofautonomy for the vehicle 700 or other machine, such as being fullyautonomous, being highly autonomous, being conditionally autonomous,being partially autonomous, providing assistive autonomy, beingsemi-autonomous, being primarily autonomous, or other designation.

The vehicle 700 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 700 may include a propulsion system750, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 750 may be connected to a drive train of the vehicle700, which may include a transmission, to enable the propulsion of thevehicle 700. The propulsion system 750 may be controlled in response toreceiving signals from the throttle/accelerator 752.

A steering system 754, which may include a steering wheel, may be usedto steer the vehicle 700 (e.g., along a desired path or route) when thepropulsion system 750 is operating (e.g., when the vehicle is inmotion). The steering system 754 may receive signals from a steeringactuator 756. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 746 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 748 and/or brakesensors.

Controller(s) 736, which may include one or more system on chips (SoCs)704 (FIG. 7C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle700. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 748, to operate thesteering system 754 via one or more steering actuators 756, to operatethe propulsion system 750 via one or more throttle/accelerators 752. Thecontroller(s) 736 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 700. The controller(s) 736 may include a first controller 736for autonomous driving functions, a second controller 736 for functionalsafety functions, a third controller 736 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 736 forinfotainment functionality, a fifth controller 736 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 736 may handle two or more of the abovefunctionalities, two or more controllers 736 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 736 may provide the signals for controlling one ormore components and/or systems of the vehicle 700 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 758 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 760, ultrasonic sensor(s) 762, LIDARsensor(s) 764, inertial measurement unit (IMU) sensor(s) 766 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 796, stereo camera(s) 768, wide-view camera(s) 770(e.g., fisheye cameras), infrared camera(s) 772, surround camera(s) 774(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 798,speed sensor(s) 744 (e.g., for measuring the speed of the vehicle 700),vibration sensor(s) 742, steering sensor(s) 740, brake sensor(s) (e.g.,as part of the brake sensor system 746), and/or other sensor types.

One or more of the controller(s) 736 may receive inputs (e.g.,represented by input data) from an instrument cluster 732 of the vehicle700 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 734, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle700. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 722 of FIG. 7C), location data(e.g., the vehicle’s 700 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 736,etc. For example, the HMI display 734 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 700 further includes a network interface 724 which may useone or more wireless antenna(s) 726 and/or modem(s) to communicate overone or more networks. For example, the network interface 724 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 726 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 7B is an example of camera locations and fields of view for theexample autonomous vehicle 700 of FIG. 7A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle700.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 700. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera’s image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 700 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 736 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (LDW), Autonomous Cruise Control(ACC), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 770 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.7B, there may any number of wide-view cameras 770 on the vehicle 700. Inaddition, long-range camera(s) 798 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 798 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 768 may also be included in a front-facingconfiguration. The stereo camera(s) 768 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle’s environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 768 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 768 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 700 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 774 (e.g., four surround cameras 774 asillustrated in FIG. 7B) may be positioned to on the vehicle 700. Thesurround camera(s) 774 may include wide-view camera(s) 770, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle’s front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 774 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 700 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 798,stereo camera(s) 768), infrared camera(s) 772, etc.), as describedherein.

FIG. 7C is a block diagram of an example system architecture for theexample autonomous vehicle 700 of FIG. 7A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 700 in FIG.7C are illustrated as being connected via bus 702. The bus 702 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 700 used to aid in control of various features and functionalityof the vehicle 700, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 702 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 702, this is notintended to be limiting. For example, there may be any number of busses702, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses702 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 702 may be used for collisionavoidance functionality and a second bus 702 may be used for actuationcontrol. In any example, each bus 702 may communicate with any of thecomponents of the vehicle 700, and two or more busses 702 maycommunicate with the same components. In some examples, each SoC 704,each controller 736, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle700), and may be connected to a common bus, such the CAN bus.

The vehicle 700 may include one or more controller(s) 736, such as thosedescribed herein with respect to FIG. 7A. The controller(s) 736 may beused for a variety of functions. The controller(s) 736 may be coupled toany of the various other components and systems of the vehicle 700, andmay be used for control of the vehicle 700, artificial intelligence ofthe vehicle 700, infotainment for the vehicle 700, and/or the like.

The vehicle 700 may include a system(s) on a chip (SoC) 704. The SoC 704may include CPU(s) 706, GPU(s) 708, processor(s) 710, cache(s) 712,accelerator(s) 714, data store(s) 716, and/or other components andfeatures not illustrated. The SoC(s) 704 may be used to control thevehicle 700 in a variety of platforms and systems. For example, theSoC(s) 704 may be combined in a system (e.g., the system of the vehicle700) with an HD map 722 which may obtain map refreshes and/or updatesvia a network interface 724 from one or more servers (e.g., server(s)778 of FIG. 7D).

The CPU(s) 706 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 706 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 706may include eight cores in a coherent multiprocessor configuration. Insome embodiments, the CPU(s) 706 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 706 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)706 to be active at any given time.

The CPU(s) 706 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 706may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 708 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 708 may be programmable and may beefficient for parallel workloads. The GPU(s) 708, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 708 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 708 may include at least eight streamingmicroprocessors. The GPU(s) 708 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 708 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA’sCUDA).

The GPU(s) 708 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 708 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 708 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 708 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 708 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 708 to access the CPU(s) 706 page tables directly. Insuch examples, when the GPU(s) 708 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 706. In response, the CPU(s) 706 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 708. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 706 and the GPU(s) 708, thereby simplifying the GPU(s) 708programming and porting of applications to the GPU(s) 708.

In addition, the GPU(s) 708 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 708 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 704 may include any number of cache(s) 712, including thosedescribed herein. For example, the cache(s) 712 may include an L3 cachethat is available to both the CPU(s) 706 and the GPU(s) 708 (e.g., thatis connected both the CPU(s) 706 and the GPU(s) 708). The cache(s) 712may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 704 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 700 - such as processingDNNs. In addition, the SoC(s) 704 may include a floating point unit(s)(FPU(s)) - or other math coprocessor or numeric coprocessor types - forperforming mathematical operations within the system. For example, theSoC(s) 704 may include one or more FPUs integrated as execution unitswithin a CPU(s) 706 and/or GPU(s) 708.

The SoC(s) 704 may include one or more accelerators 714 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 704 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 708 and to off-load some of the tasks of theGPU(s) 708 (e.g., to free up more cycles of the GPU(s) 708 forperforming other tasks). As an example, the accelerator(s) 714 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 714 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 708, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 708 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 708 and/or other accelerator(s) 714.

The accelerator(s) 714 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 706. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 714 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 714. In someexamples, the on-chip memory may include at least 4MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 704 may include a real-time ray-tracinghardware accelerator, such as described in U.S. Pat. Application No.16/101,232, filed on Aug. 10, 2018. The real-time ray-tracing hardwareaccelerator may be used to quickly and efficiently determine thepositions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 714 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA’s capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 766 output thatcorrelates with the vehicle 700 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 764 or RADAR sensor(s) 760), amongothers.

The SoC(s) 704 may include data store(s) 716 (e.g., memory). The datastore(s) 716 may be on-chip memory of the SoC(s) 704, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 716 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 712 may comprise L2 or L3 cache(s) 712. Reference to thedata store(s) 716 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 714, as described herein.

The SoC(s) 704 may include one or more processor(s) 710 (e.g., embeddedprocessors). The processor(s) 710 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 704 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 704 thermals and temperature sensors, and/ormanagement of the SoC(s) 704 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 704 may use thering-oscillators to detect temperatures of the CPU(s) 706, GPU(s) 708,and/or accelerator(s) 714. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 704 into a lower powerstate and/or put the vehicle 700 into a chauffeur to safe stop mode(e.g., bring the vehicle 700 to a safe stop).

The processor(s) 710 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 710 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 710 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 710 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 710 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 710 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)770, surround camera(s) 774, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle’s destination,activate or change the vehicle’s infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 708 is not required tocontinuously render new surfaces. Even when the GPU(s) 708 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 708 to improve performance and responsiveness.

The SoC(s) 704 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 704 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 704 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 704 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 764, RADAR sensor(s) 760,etc. that may be connected over Ethernet), data from bus 702 (e.g.,speed of vehicle 700, steering wheel position, etc.), data from GNSSsensor(s) 758 (e.g., connected over Ethernet or CAN bus). The SoC(s) 704may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 706 from routine data management tasks.

The SoC(s) 704 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 704 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 714, when combined with the CPU(s) 706, the GPU(s) 708,and the data store(s) 716, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 720) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle’s path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle’s path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 708.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 700. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 704 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 796 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 704 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)758. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 762, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 718 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 704 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 718 may include an X86 processor,for example. The CPU(s) 718 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 704, and/or monitoring the statusand health of the controller(s) 736 and/or infotainment SoC 730, forexample.

The vehicle 700 may include a GPU(s) 720 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 704 via a high-speedinterconnect (e.g., NVIDIA’s NVLINK). The GPU(s) 720 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 700.

The vehicle 700 may further include the network interface 724 which mayinclude one or more wireless antennas 726 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 724 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 778 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 700information about vehicles in proximity to the vehicle 700 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 700).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 700.

The network interface 724 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 736 tocommunicate over wireless networks. The network interface 724 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 700 may further include data store(s) 728 which may includeoff-chip (e.g., off the SoC(s) 704) storage. The data store(s) 728 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 700 may further include GNSS sensor(s) 758. The GNSSsensor(s) 758 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS)sensors, etc.), to assist in mapping, perception, occupancy gridgeneration, and/or path planning functions. Any number of GNSS sensor(s)758 may be used, including, for example and without limitation, a GPSusing a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle 700 may further include RADAR sensor(s) 760. The RADARsensor(s) 760 may be used by the vehicle 700 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 760 may usethe CAN and/or the bus 702 (e.g., to transmit data generated by theRADAR sensor(s) 760) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 760 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 760 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 760may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle’s 700 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle’s 700 lane.

Mid-range RADAR systems may include, as an example, a range of up to 760m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 750 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 700 may further include ultrasonic sensor(s) 762. Theultrasonic sensor(s) 762, which may be positioned at the front, back,and/or the sides of the vehicle 700, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 762 may be used, and different ultrasonic sensor(s) 762 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 762 may operate at functional safety levels of ASILB.

The vehicle 700 may include LIDAR sensor(s) 764. The LIDAR sensor(s) 764may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 764 maybe functional safety level ASIL B. In some examples, the vehicle 700 mayinclude multiple LIDAR sensors 764 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 764 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 764 may have an advertised rangeof approximately 700 m, with an accuracy of 2 cm-3 cm, and with supportfor a 700 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 764 may be used. In such examples,the LIDAR sensor(s) 764 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 700.The LIDAR sensor(s) 764, in such examples, may provide up to a120-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)764 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 700. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)764 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 766. The IMU sensor(s) 766may be located at a center of the rear axle of the vehicle 700, in someexamples. The IMU sensor(s) 766 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 766 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 766 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 766 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electromechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 766 may enable the vehicle 700to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 766. In some examples, the IMU sensor(s) 766 and theGNSS sensor(s) 758 may be combined in a single integrated unit.

The vehicle may include microphone(s) 796 placed in and/or around thevehicle 700. The microphone(s) 796 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 768, wide-view camera(s) 770, infrared camera(s) 772,surround camera(s) 774, long-range and/or mid-range camera(s) 798,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 700. The types of cameras useddepends on the embodiments and requirements for the vehicle 700, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 700. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 7A and FIG. 7B.

The vehicle 700 may further include vibration sensor(s) 742. Thevibration sensor(s) 742 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 742 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 700 may include an ADAS system 738. The ADAS system 738 mayinclude a SoC, in some examples. The ADAS system 738 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 760, LIDAR sensor(s) 764, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 700 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 700 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 724 and/or the wireless antenna(s) 726 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 700), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 700, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 760, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 760, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle700 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 700 if the vehicle 700 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile’sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)760, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 700 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 760, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 700, the vehicle 700itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 736 or a second controller 736). For example, in someembodiments, the ADAS system 738 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 738may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer’s confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer’s direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer’s output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 704.

In other examples, ADAS system 738 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 738 may be fed into theprimary computer’s perception block and/or the primary computer’sdynamic driving task block. For example, if the ADAS system 738indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 700 may further include the infotainment SoC 730 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 730 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 700. For example, the infotainment SoC 730 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 734, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 730 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 738,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 730 may include GPU functionality. The infotainmentSoC 730 may communicate over the bus 702 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 700. Insome examples, the infotainment SoC 730 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 736(e.g., the primary and/or backup computers of the vehicle 700) fail. Insuch an example, the infotainment SoC 730 may put the vehicle 700 into achauffeur to safe stop mode, as described herein.

The vehicle 700 may further include an instrument cluster 732 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 732 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 732 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 730 and theinstrument cluster 732. In other words, the instrument cluster 732 maybe included as part of the infotainment SoC 730, or vice versa.

FIG. 7D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 700 of FIG. 7A, inaccordance with some embodiments of the present disclosure. The system776 may include server(s) 778, network(s) 790, and vehicles, includingthe vehicle 700. The server(s) 778 may include a plurality of GPUs784(A)-784(H) (collectively referred to herein as GPUs 784), PCIeswitches 782(A)-782(H) (collectively referred to herein as PCIe switches782), and/or CPUs 780(A)-780(B) (collectively referred to herein as CPUs780). The GPUs 784, the CPUs 780, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 788 developed by NVIDIA and/orPCIe connections 786. In some examples, the GPUs 784 are connected viaNVLink and/or NVSwitch SoC and the GPUs 784 and the PCIe switches 782are connected via PCIe interconnects. Although eight GPUs 784, two CPUs780, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 778 mayinclude any number of GPUs 784, CPUs 780, and/or PCIe switches. Forexample, the server(s) 778 may each include eight, sixteen, thirty-two,and/or more GPUs 784.

The server(s) 778 may receive, over the network(s) 790 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 778 may transmit, over the network(s) 790 and to the vehicles,neural networks 792, updated neural networks 792, and/or map information794, including information regarding traffic and road conditions. Theupdates to the map information 794 may include updates for the HD map722, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 792, the updated neural networks 792, and/or the mapinformation 794 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 778 and/or other servers).

The server(s) 778 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training,self-learning, reinforcement learning, federated learning, transferlearning, feature learning (including principal component and clusteranalyses), multi-linear subspace learning, manifold learning,representation learning (including spare dictionary learning),rule-based machine learning, anomaly detection, and any variants orcombinations therefor. Once the machine learning models are trained, themachine learning models may be used by the vehicles (e.g., transmittedto the vehicles over the network(s) 790, and/or the machine learningmodels may be used by the server(s) 778 to remotely monitor thevehicles.

In some examples, the server(s) 778 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 778 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 784, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 778 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 778 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 700. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 700, suchas a sequence of images and/or objects that the vehicle 700 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 700 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 700 is malfunctioning, the server(s) 778 may transmit asignal to the vehicle 700 instructing a fail-safe computer of thevehicle 700 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 778 may include the GPU(s) 784 and one ormore programmable inference accelerators (e.g., NVIDIA’s TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 8 is a block diagram of an example computing device(s) 800 suitablefor use in implementing some embodiments of the present disclosure.Computing device 800 may include an interconnect system 802 thatdirectly or indirectly couples the following devices: memory 804, one ormore central processing units (CPUs) 806, one or more graphicsprocessing units (GPUs) 808, a communication interface 810, input/output(I/O) ports 812, input/output components 814, a power supply 816, one ormore presentation components 818 (e.g., display(s)), and one or morelogic units 820. In at least one embodiment, the computing device(s) 800may comprise one or more virtual machines (VMs), and/or any of thecomponents thereof may comprise virtual components (e.g., virtualhardware components). For non-limiting examples, one or more of the GPUs808 may comprise one or more vGPUs, one or more of the CPUs 806 maycomprise one or more vCPUs, and/or one or more of the logic units 820may comprise one or more virtual logic units. As such, a computingdevice(s) 800 may include discrete components (e.g., a full GPUdedicated to the computing device 800), virtual components (e.g., aportion of a GPU dedicated to the computing device 800), or acombination thereof.

Although the various blocks of FIG. 8 are shown as connected via theinterconnect system 802 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 818, such as a display device, may be consideredan I/O component 814 (e.g., if the display is a touch screen). Asanother example, the CPUs 806 and/or GPUs 808 may include memory (e.g.,the memory 804 may be representative of a storage device in addition tothe memory of the GPUs 808, the CPUs 806, and/or other components). Inother words, the computing device of FIG. 8 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.8 .

The interconnect system 802 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 802 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 806 may be directly connectedto the memory 804. Further, the CPU 806 may be directly connected to theGPU 808. Where there is direct, or point-to-point connection betweencomponents, the interconnect system 802 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 800.

The memory 804 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 800. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 804 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device800. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 806 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 800 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 806 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 806 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 800 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 800, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 800 mayinclude one or more CPUs 806 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 806, the GPU(s) 808 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device800 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 808 may be an integrated GPU (e.g.,with one or more of the CPU(s) 806 and/or one or more of the GPU(s) 808may be a discrete GPU. In embodiments, one or more of the GPU(s) 808 maybe a coprocessor of one or more of the CPU(s) 806. The GPU(s) 808 may beused by the computing device 800 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 808 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 808may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 808may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 806 received via ahost interface). The GPU(s) 808 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory804. The GPU(s) 808 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 808 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 806 and/or the GPU(s)808, the logic unit(s) 820 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 800 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 806, the GPU(s)808, and/or the logic unit(s) 820 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 820 may be part of and/or integrated in one ormore of the CPU(s) 806 and/or the GPU(s) 808 and/or one or more of thelogic units 820 may be discrete components or otherwise external to theCPU(s) 806 and/or the GPU(s) 808. In embodiments, one or more of thelogic units 820 may be a coprocessor of one or more of the CPU(s) 806and/or one or more of the GPU(s) 808.

Examples of the logic unit(s) 820 include one or more processing coresand/or components thereof, such as Data Processing Units (DPUs), TensorCores (TCs), Tensor Processing Units(TPUs), Pixel Visual Cores (PVCs),Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs),Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs),Tree Traversal Units (TTUs), Artificial Intelligence Accelerators(AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units(ALUs), Application-Specific Integrated Circuits (ASICs), Floating PointUnits (FPUs), input/output (I/O) elements, peripheral componentinterconnect (PCI) or peripheral component interconnect express (PCIe)elements, and/or the like.

The communication interface 810 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 800to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 810 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet. In one or more embodiments, logic unit(s) 820and/or communication interface 810 may include one or more dataprocessing units (DPUs) to transmit data received over a network and/orthrough interconnect system 802 directly to (e.g., a memory of) one ormore GPU(s) 808.

The I/O ports 812 may enable the computing device 800 to be logicallycoupled to other devices including the I/O components 814, thepresentation component(s) 818, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 800.Illustrative I/O components 814 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 814 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 800. Thecomputing device 800 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 800 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 800 to render immersive augmented reality or virtual reality.

The power supply 816 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 816 may providepower to the computing device 800 to enable the components of thecomputing device 800 to operate.

The presentation component(s) 818 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 818 may receivedata from other components (e.g., the GPU(s) 808, the CPU(s) 806, DPUs,etc.), and output the data (e.g., as an image, video, sound, etc.).

Example Data Center

FIG. 9 illustrates an example data center 900 that may be used in atleast one embodiments of the present disclosure. The data center 900 mayinclude a data center infrastructure layer 910, a framework layer 920, asoftware layer 930, and/or an application layer 940.

As shown in FIG. 9 , the data center infrastructure layer 910 mayinclude a resource orchestrator 912, grouped computing resources 914,and node computing resources (“node C.R.s”) 916(1)-916(N), where “N”represents any whole, positive integer. In at least one embodiment, nodeC.R.s 916(1)-916(N) may include, but are not limited to, any number ofcentral processing units (CPUs) or other processors (including DPUs,accelerators, field programmable gate arrays (FPGAs), graphicsprocessors or graphics processing units (GPUs), etc.), memory devices(e.g., dynamic read-only memory), storage devices (e.g., solid state ordisk drives), network input/output (NW I/O) devices, network switches,virtual machines (VMs), power modules, and/or cooling modules, etc. Insome embodiments, one or more node C.R.s from among node C.R.s916(1)-916(N) may correspond to a server having one or more of theabove-mentioned computing resources. In addition, in some embodiments,the node C.R.s 916(1)-9161(N) may include one or more virtualcomponents, such as vGPUs, vCPUs, and/or the like, and/or one or more ofthe node C.R.s 916(1)-916(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources 914 may includeseparate groupings of node C.R.s 916 housed within one or more racks(not shown), or many racks housed in data centers at variousgeographical locations (also not shown). Separate groupings of nodeC.R.s 916 within grouped computing resources 914 may include groupedcompute, network, memory or storage resources that may be configured orallocated to support one or more workloads. In at least one embodiment,several node C.R.s 916 including CPUs, GPUs, DPUs, and/or otherprocessors may be grouped within one or more racks to provide computeresources to support one or more workloads. The one or more racks mayalso include any number of power modules, cooling modules, and/ornetwork switches, in any combination.

The resource orchestrator 912 may configure or otherwise control one ormore node C.R.s 916(1)-916(N) and/or grouped computing resources 914. Inat least one embodiment, resource orchestrator 912 may include asoftware design infrastructure (SDI) management entity for the datacenter 900. The resource orchestrator 912 may include hardware,software, or some combination thereof.

In at least one embodiment, as shown in FIG. 9 , framework layer 920 mayinclude a job scheduler 932, a configuration manager 934, a resourcemanager 936, and/or a distributed file system 938. The framework layer920 may include a framework to support software 932 of software layer930 and/or one or more application(s) 942 of application layer 940. Thesoftware 932 or application(s) 942 may respectively include web-basedservice software or applications, such as those provided by Amazon WebServices, Google Cloud and Microsoft Azure. The framework layer 920 maybe, but is not limited to, a type of free and open-source software webapplication framework such as Apache Spark™ (hereinafter “Spark”) thatmay utilize distributed file system 938 for large-scale data processing(e.g., “big data”). In at least one embodiment, job scheduler 932 mayinclude a Spark driver to facilitate scheduling of workloads supportedby various layers of data center 900. The configuration manager 934 maybe capable of configuring different layers such as software layer 930and framework layer 920 including Spark and distributed file system 938for supporting large-scale data processing. The resource manager 936 maybe capable of managing clustered or grouped computing resources mappedto or allocated for support of distributed file system 938 and jobscheduler 932. In at least one embodiment, clustered or groupedcomputing resources may include grouped computing resource 914 at datacenter infrastructure layer 910. The resource manager 936 may coordinatewith resource orchestrator 912 to manage these mapped or allocatedcomputing resources.

In at least one embodiment, software 932 included in software layer 930may include software used by at least portions of node C.R.s916(1)-916(N), grouped computing resources 914, and/or distributed filesystem 938 of framework layer 920. One or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 942 included in applicationlayer 940 may include one or more types of applications used by at leastportions of node C.R.s 916(1)-916(N), grouped computing resources 914,and/or distributed file system 938 of framework layer 920. One or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.),and/or other machine learning applications used in conjunction with oneor more embodiments.

In at least one embodiment, any of configuration manager 934, resourcemanager 936, and resource orchestrator 912 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. Self-modifying actions mayrelieve a data center operator of data center 900 from making possiblybad configuration decisions and possibly avoiding underutilized and/orpoor performing portions of a data center.

The data center 900 may include tools, services, software or otherresources to train one or more machine learning models or predict orinfer information using one or more machine learning models according toone or more embodiments described herein. For example, a machinelearning model(s) may be trained by calculating weight parametersaccording to a neural network architecture using software and/orcomputing resources described above with respect to the data center 900.In at least one embodiment, trained or deployed machine learning modelscorresponding to one or more neural networks may be used to infer orpredict information using resources described above with respect to thedata center 900 by using weight parameters calculated through one ormore training techniques, such as but not limited to those describedherein.

In at least one embodiment, the data center 900 may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, and/orother hardware (or virtual compute resources corresponding thereto) toperform training and/or inferencing using above-described resources.Moreover, one or more software and/or hardware resources described abovemay be configured as a service to allow users to train or performinginferencing of information, such as image recognition, speechrecognition, or other artificial intelligence services.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of thecomputing device(s) 800 of FIG. 8 - e.g., each device may includesimilar components, features, and/or functionality of the computingdevice(s) 800. In addition, where backend devices (e.g., servers, NAS,etc.) are implemented, the backend devices may be included as part of adata center 900, an example of which is described in more detail hereinwith respect to FIG. 9 .

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments - in which case a server may not be included in anetwork environment - and one or more client-server networkenvironments - in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example computing device(s) 800described herein with respect to FIG. 8 . By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A processor comprising: one or more circuits to:compute, using a machine learning model and based at least in part onsensor data generated using one or more sensors of an ego-machine, dataindicative of a visibility distance corresponding to the sensor data;determine, based at least in part on the visibility distance, ausability of the sensor data for one or more operations of theego-machine; and perform at least one operation of the one or moreoperations based at least on part on the usability of the sensor data.2. The processor of claim 1, further comprising determining to refrainfrom performing at least one autonomous or semi-autonomous operation ofthe one or more operations based at least in part on the usability ofthe sensor data.
 3. The processor of claim 1, wherein the computation ofthe data includes regressing on a value of the visibility distance. 4.The processor of claim 3, further comprising: determining, based atleast in part on the value of the visibility distance, a visibilitydistance bin from a plurality of visibility distance bins correspondingto the sensor data, wherein the visibility distance bin is indicative ofthe usability of the sensor data for the one or more operations of theego-machine.
 5. The processor of claim 1, wherein the data is indicativeof a visibility distance bin of a plurality of visibility distance bins,and further wherein the visibility distance bin is indicative of theusability of the sensor data for the one or more operations of theego-machine.
 6. The processor of claim 1, wherein the one or moreoperations include at least one of object tracking, object detection,path planning, obstacle avoidance, or an advanced driver assistancesystem (ADAS) operation.
 7. The processor of claim 1, wherein themachine learning model includes a deep neural network (DNN), the DNNtrained using a combination of real-world data, augmented real-worlddata, and synthetic data.
 8. The processor of claim 1, wherein theprocessor is comprised in at least one of: a control system for anautonomous or semi-autonomous machine; a perception system for anautonomous or semi-autonomous machine; a system for performingsimulation operations; a system for performing deep learning operations;a system implemented using an edge device; a system implemented using arobot; a system incorporating one or more virtual machines (VMs); asystem implemented at least partially in a data center; or a systemimplemented at least partially using cloud computing resources.
 9. Asystem comprising: one or more sensors; and one or more processing unitsto determine a usability of instances of sensor data generated using theone or more sensors based at least in part on associated visibilitydistances computed using a machine learning model, the machine learningmodel trained at least in part by: receiving values corresponding to oneor more parameters for adjusting visibility corresponding to an instanceof training sensor data; generating the instance of training sensor databased at least in part on the values; determining a visibility distancecorresponding to the instance of training sensor data based at least inpart on the values; and training a machine learning model using theinstance of training sensor data and the visibility distance as groundtruth data.
 10. The system of claim 9, wherein the parameters correspondto a weather condition, a lighting condition, or an occlusion condition.11. The system of claim 9, wherein the determining the visibilitydistance includes using the values to compute the visibility distance.12. The system of claim 11, wherein the values are applied to a trainedmodel that determines a correspondence between respective values of theone or more parameters and visibility distances.
 13. The system of claim12, wherein the trained model is trained using annotations or labelsindicating visibility distances corresponding to instances of trainingsensor data generated using associated values of the one or moreparameters.
 14. The system of claim 9, wherein the instance of trainingsensor data corresponds to an instance of real-world sensor data, andthe generating the instance of training sensor data includes augmentingthe instance of real-world sensor data using the values corresponding tothe one or more parameters to generate the instance of training sensordata.
 15. The system of claim 9, wherein the instance of training sensordata corresponds to a synthetic instance of sensor data, and thegenerating the synthetic instance of sensor data includes: applying thevalues corresponding to the one or more parameters to a simulationengine to generate a simulation; and capturing the synthetic instance ofsensor data using one or more virtual sensors within the simulation. 16.The system of claim 9, wherein the machine learning model is trained tocompute data indicative of visibility distances, and the trainingincludes using a loss function that penalizes over-estimation of thevisibility distances more than under-estimation of the visibilitydistances.
 17. The system of claim 9, wherein the system is comprised inat least one of: a control system for an autonomous or semi-autonomousmachine; a perception system for an autonomous or semi-autonomousmachine; a system for performing simulation operations; a system forperforming deep learning operations; a system implemented using an edgedevice; a system implemented using a robot; a system incorporating oneor more virtual machines (VMs); a system implemented at least partiallyin a data center; or a system implemented at least partially using cloudcomputing resources.
 18. A processor comprising: processing circuitry todetermine a set of suitable operations of a machine corresponding to aninstance of sensor data based at least in part on a visibility distanceassociated with the instance of sensor data as computed using a machinelearning model.
 19. The processor of claim 18, wherein at least oneoperation is not included in the set of suitable operations based atleast in part on the visibility distance being below a thresholdvisibility distance.
 20. The processor of claim 18, wherein the set ofsuitable operations is determined based at least in part on thevisibility distance falling within a visibility distance bincorresponding to the set of suitable operations.